From the Departments of Radiology (A.B., F.P.), Medical Oncology (M.B., J.Y.B.), and Surgery (F.G.), Centre Léon Bérard, 28 Prom. Léa Et Napoléon Bullukian, 69008 Lyon, France; Université de Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, Centre Léon Bérard, CREATIS UMR 5220, U1206, Lyon, France (A.B., B.L., A.N., O.B., F.P.); Department of Radiology, AP-HP Hôpital Cochin, Paris, France (W.K., J.L.D.); Department of Pediatric Oncology, Institut d'Hématologie et d'Oncologie Pédiatrique, Lyon, France (J.D., P.M.B.); and Department of Radiology, Centre Hospitalier Universitaire de Nantes, Nantes, France (A.B.V.).
Radiol Imaging Cancer. 2022 Sep;4(5):e210107. doi: 10.1148/rycan.210107.
Histologic response to chemotherapy for osteosarcoma is one of the most important prognostic factors for survival, but assessment occurs after surgery. Although tumor imaging is used for surgical planning and follow-up, it lacks predictive value. Therefore, a radiomics model was developed to predict the response to neoadjuvant chemotherapy based on pretreatment T1-weighted contrast-enhanced MRI. A total of 176 patients (median age, 20 years [range, 5-71 years]; 107 male patients) with osteosarcoma treated with neoadjuvant chemotherapy and surgery between January 2007 and December 2018 in three different centers in France (Centre Léon Bérard in Lyon, Centre Hospitalier Universitaire de Nantes in Nantes, and Hôpital Cochin in Paris) were retrospectively analyzed. Various models were trained from different configurations of the data sets. Two different methods of feature selection were tested with and without ComBat harmonization (ReliefF and test) to select the most relevant features, and two different classifiers were used to build the models (an artificial neural network and a support vector machine). Sixteen radiomics models were built using the different combinations of feature selection and classifier applied on the various data sets. The most predictive model had an area under the receiver operating characteristic curve of 0.95, a sensitivity of 91%, and a specificity 92% in the training set; respective values in the validation set were 0.97, 91%, and 92%. In conclusion, MRI-based radiomics may be useful to stratify patients receiving neoadjuvant chemotherapy for osteosarcomas. MRI, Skeletal-Axial, Oncology, Radiomics, Osteosarcoma, Pediatrics © RSNA, 2022.
骨肉瘤的化疗反应是影响生存的最重要的预后因素之一，但评估是在手术后进行的。尽管肿瘤影像学用于手术计划和随访，但它缺乏预测价值。因此，我们开发了一种基于预处理 T1 加权对比增强 MRI 的放射组学模型，以预测新辅助化疗的反应。我们回顾性分析了 2007 年 1 月至 2018 年 12 月在法国三个不同中心（里昂的 Centre Léon Bérard、南特的 Centre Hospitalier Universitaire de Nantes 和巴黎的 Hôpital Cochin）接受新辅助化疗和手术治疗的 176 例骨肉瘤患者（中位年龄 20 岁[范围：5-71 岁]；男性 107 例）。从不同数据集的不同配置中训练了各种模型。我们测试了两种不同的特征选择方法，一种是带 ComBat 协调（ReliefF 和 t 检验）的特征选择，另一种是不带 ComBat 协调的特征选择，以选择最相关的特征，并使用两种不同的分类器构建模型（人工神经网络和支持向量机）。使用不同的特征选择和分类器组合在不同的数据集中构建了 16 个放射组学模型。在训练集中，预测能力最佳的模型的受试者工作特征曲线下面积为 0.95，敏感性为 91%，特异性为 92%；在验证集中，相应的值分别为 0.97、91%和 92%。总之，基于 MRI 的放射组学可能有助于对接受骨肉瘤新辅助化疗的患者进行分层。
